SACTI model in prediction and assessment of large scale natural draft cooling tower environmental impact of nuclear power plant

Large Scale Natural Draft Cooling Tower has become a hot topic in China because it is an important part of the nuclear power plant, and its environmental impacts include shading, solar energy loss, water deposition and salt deposition. In China, there is no built large-scale natural draft cooling tower of nuclear power plant. Therefore, model prediction becomes an effective way to solve this problem. This paper introduces the basic principles and structure of SACTI (Seasonal and Annual Cooling Tower Impact) model. SACTI is a cooling tower assessment model developed by Argonne National Laboratory, USA. A comparative case study between China's Pengze Nuclear Power Plant and the US Amos Power Plant is also presented. Calculations were carried out for the Pengze and Amos power plants, and the results showed that the maximum value of salt deposition at the Pengze plant was about 166.5 kg/(km2-month) at a distance of 800 m from the cooling tower. The maximum value of salt deposition at the Amos plant was about 92.85 kg/(km2-month) at a distance of 600 m from the cooling tower. Conclusions show that the research work can provide a useful solution in future work, the simulation results of the SACTI model have a potential mean in the absence of monitoring data. This research provides a way to generate simulation data through SACTI program in the design process of nuclear power plant cooling tower, and designers can use these data to determine how the cooling tower will affect the natural environment and manage within an appropriate range to reduce the impact on the environment.


Overview of the SACTI model
Introduction to the model. The SACTI model, which is known as Seasonal and Annual Cooling Tower Impact (SACTI) 19,20 , was developed by Argonne National Laboratory as a part of the EPRI (Electric Power Research Institute) project.
The basic principle of the model is the cooling tower plume prediction model. Factors influencing the diffusion of the cooling tower plume include meteorological elements, topography, nearby buildings and the height of the cooling tower [21][22][23][24][25] . In addition, ambient temperature and wind speed directly affect the water vapour height and the diffusion range of the plume. Therefore, under different conditions of ambient temperature and wind speed, the SACTI model can simulate the variation of the characteristic parameters of the plume. In this way, the SACTI model can be used to analyse how the plume will react to different ambient temperatures and wind speeds and what the possible effects will be [26][27][28] .
The SACTI model can now calculate the environmental impact of a single tower as well as multiple towers lined up in a given direction. The results include the length-frequency distribution of the plume in a subzone of 16 directional and distance segments around the cooling tower, and the distribution of cumulative solar radiation loss, water deposition and salt deposition over the year.
The SACTI model includes humidity and temperature as data inputs, and the current effects of humidity and temperature on plume dispersion are reflected in the fact that as temperature increases and humidity decreases, water droplets in the plume will evaporate faster, and these effects are considered in the SACTI model.
In order to verify the accuracy of the model, Carhart 29,30 used 39 sets of observed data from single towers and 26 sets of observed data from multiple towers to compare the simulated data from SACTI for the study. In 60% of the cases, SACTI was found to be able to control the error factor to within 2. For the shadow effect, the SACTI model takes into account the temporal shape, direction and optical thickness of the fog plume. Thus, it can reflect the radiation loss caused by the fog plume more realistically than previous models.
It is worth noting that SACTI tends to overestimate the fog plume length, and this bi-conservative result is useful in environmental impact assessment.
Basic structure of the model and input parameters. The SACTI model consists of four relatively independent subroutines to implement the model calculation. These are the pre-processing module (PREP), the plume calculation module (MULT), the table output module (TABLE) and the plotting module (PAGEPLOT), and the four modules are run sequentially.
The main structure of the SACTI model is shown in Fig. 1.  www.nature.com/scientificreports/ The pre-processing (PREP) module mainly filters the input data and removes the invalid and incorrect data to generate typical conditions for cooling tower outlet section conditions, with a total of 35 conditions generated in the SACTI model. The pre-processor requires three types of data input from the user: hour-by-hour meteorological data (CD144.TAP), day-by-day mixed layer height data (MIXHT.TAP), and source term data (PREP.USR). The plume calculation (MULT) module calculates the impact range of plume and drift using typical operating conditions for each type of plume. The input file (MULT.USR) for this module is relatively simple and allows the user to customize the number of cooling towers and the relative coordinates of each cooling tower. The TABLE  module use an enhanced database and plume calculation program to generate the final results and produce a  text data table that reflects the environmental impact at different wind directions and different seasons. The input  control file of the module is TABLES.USR, which can be controlled by the user according to the needs of output. For example, when the user wants to output the distance segment interval or vertical height segment interval, the output results of the module are saved in the Table.OUT file. PAGEPLOT program will print out the results in the table program, the format of the output is ASCII graphics file PAGE.OUT.
Validation of the SACTI model. This paper uses experimental data from Chalkpoint Power Station 29 to verify the validity of the SACTI model. Although extensive field tracer tests of salt deposition have been carried out at Chalkpoint, the results had often been unsatisfactory due to the influence of other nearby stack emissions. The most favourable test conditions were achieved on 16 June 1977. The results of this tracer test are used in this research for the model validation analysis.
The ChalkPoint plant tracer experiment used a single dose of 30 gallons of 20% tracer into the drench tank at the base of the cooling tower with no water replenishment or drainage, so the only means of moisture loss was through the cooling tower mist plume. The concentration of tracer in the drench tank was kept constant throughout the experiment and the plant operating load remained stable throughout the experiment. The source measurement report shows that the cooling tower plume caused a moisture loss of 0.002%. The plume temperature was 315.3 K, the ambient temperature was 295.3 K, the plume discharge rate was 4.5 m/s, the tracer discharge rate was 1.86 g/s, the ambient humidity during the test was as high as 93%, so the evaporation of water droplets in the plume could be ignored, the wind direction during the test was south, so the cooling tower plume diffusion basically did not receive the influence of other plant site structures, the wind speed during the test period at a height of 100 m is about 8 m/s, below 100 m, the wind speed approximately obeys the exponential distribution, the wind speed at a height of 50 m is about 5 m/s. The monitoring point layout to the cooling tower as the central point, the distance from the cooling tower 0.5 km and 1.0 km set 35° arc, every 5° set a sampling point.
The surface wet deposition concentrations at these two distances were used for the validation analysis, as the most favourable results from the ChalkPoint experiment were obtained for the 500 m and 1000 m arc sampling data. Figure 2 shows a comparison of the SACTI results with the experimental results.
The SACTI calculations are slightly higher than the ChalkPoint experiment results at 500 m and in better agreement at 1000 m, as shown in Fig. 2. The maximum wet deposition concentration at ground level with a wet deposition of 1.35E−07 kg/m 2 s occurs at 620 m from the SACTI calculations.
The ChalkPoint power plant experiment is the most successful experiment for wet plume deposition in large naturally ventilated cooling towers. For wet deposition sampling, the ChalkPoint power plant experiment has two arc sampling points and this validation has been carried out using data from the largest monitoring point on the two arcs, and there are two experimental data points in Fig. 2. This validation process is more similar to that carried out by Meroney 31 . which also used 2-point data to validate. Again, the calculated results of the SACTI model are in good agreement with the experimental results, as shown by the validation results of this paper. www.nature.com/scientificreports/ Application and analysis of SACTI model. This paper applies the SACTI model to predict the environmental impact of two large natural ventilation cooling towers of the Jiangxi Pengze nuclear power plant, and the simulation results of the SACTI model at the Amos power plant in Chicago, USA, are analogously analyzed with the prediction results of this paper.
Meteorological data. Due to the relatively long operation of the Amos power plant, there is no longer access to the latest meteorological data, and only meteorological data from 1981 are available for reference. The observation data of Pengze power plant are only publicly available for 2009, so these meteorological data are used in this study. Jiangxi Pengze nuclear power plant site is located in the south of China, the average relative humidity of the area where the plant is located in 2009 is 78.5%, the average annual wind speed is 3.3 m/s, the dominant wind direction is NE wind direction throughout the year, Fig. 3 shows the annual wind rose map of the area where Jiangxi nuclear power plant site is located, this paper uses the meteorological data observed by local weather stations. The Pengze nuclear power plant site is located in the Jiangxi province of China and is one of the first nuclear power plant sites to be built in China. After the Fukushima nuclear accident, the construction of the Pengze site was stopped due to the change in Chinese policy. Therefore, the latest meteorological data of the Pengze site is only available for 2009. In addition, this paper mainly describes and discusses the functions of the SACTI model. It does not analyse the effectiveness of specific projects. Therefore, as long as the meteorological data of the plant site can guarantee 8760 h of data to meet the computational requirements of the SACTI model, the analysis results presented in this paper are still reasonable and credible.  Table 1 during SACTI calculation.

Simulation results
The simulation flow chart of this paper is as shown in  Figure 6 shows that the cooling tower plume length frequency distribution of the Pengze nuclear power plant mainly occurs at the geometric center of the cooling tower in the SW and N directions, which is basically consistent with the wind frequency distribution in the wind rose diagram shown in Fig. 3 i.e., the area with the largest cooling tower plume length frequency occurs in the downwind direction of the dominant wind direction throughout the year at the site, mainly concentrated in the range of 3000 m from the cooling tower. The cooling www.nature.com/scientificreports/ tower plume length frequency distribution of Amos power plant shown in Fig. 7 has a similar pattern to that of Pengze power plant, which is consistent with the wind frequency distribution shown in Fig. 4, but the distance range of the cooling tower plume length frequency distribution of Amos power plant is about 6000 m, which is twice as far as that of Pengze nuclear power plant. When comparing the annual wind speed of the two sites, we can find that the annual average wind speed of Amos power plant is 7.4 m/s, while the average annual wind speed of the Pengze nuclear power plant is only 3.3 m/s, indicating that the length of the cooling tower plume is largely influenced by the wind speed. From Fig. 8, we can find out that the plume shadowing phenomenon caused by the cooling tower plume of Pengze NPP mainly occurs in the WSW and ENE directions, and from Fig. 8, the plume shadowing of Amos NPP mainly occurs in the WSW and NNE directions, which may be related to the relative humidity of the atmosphere at the site of the nuclear power plant. The percentage of solar radiation loss shown in Figs. 10 and 11 is basically consistent with the distribution of plume shadowing, and also shows that the plume shadowing phenomenon is the main cause of solar radiation loss. The maximum solar radiation loss caused by the cooling tower plume of both Pengze Nuclear Power Plant and Amos Power Plant occurs at a distance of 200 m from the cooling tower, and the corresponding reduction in solar radiation energy is 1005 MJ/m 2 and 1231.5 MJ/m 2 , accounting for about 2.78% and 3.94% of the total solar radiation energy. The natural interannual fluctuation range of solar radiation is about 1% to 10%. The shading caused by the cooling tower plume during normal operation of the nuclear power plant is mainly concentrated in a limited area around the plant site, and the maximum solar radiation energy loss caused by shading is only 2.78% and 3.94% of the solar radiation, which is within the natural inter-annual fluctuation range of solar radiation. Therefore, it is expected that the formation of "plume shadowing" from the cooling towers plume will generally have no significant impact on the surrounding environment and terrestrial ecology.
Droplet and salt deposition. The measured droplet spectra of Chalk Point Power Plant in the United States 29,30 showed that the droplet diameters of naturally ventilated cooling towers are in the range of about 10-2000 μm, mainly concentrated in the diameter range of 10-70 μm, accounting for about 56% of the total mass.  www.nature.com/scientificreports/ It can be seen from Fig. 12 that the ground deposition water caused by the cooling tower plume at the Pengze nuclear power plant is mainly distributed in the SW and N directions at the geometric center of the cooling tower, which is consistent with the length frequency distribution of the plume in Fig. 5, and both directions are downwind of the main wind direction of the site throughout the year, which also indicates that the main factor affecting the distribution of ground deposition water is the wind frequency. Figure 13 shows the distribution of ground-deposited water caused by the cooling tower plume of Amos power plant, which is basically consistent with the plume length frequency distribution shown in Fig. 7. The maximum value of ground-deposited water at the Pengze nuclear power plant occurs at 800 m from the cooling tower, which is about 3.0E+04 kg/(km 2 ·month), equivalent to an increase in precipitation of 0.4 mm/year, while the maximum value of ground deposited water at the Amos power plant occurs at 600 m from the cooling tower, which is about 1.50E+04 kg/(km 2 ·month), equivalent to an increase in precipitation of 0.18 mm/year. Comparing the design parameters of the two cooling towers listed in Table 1, it can be found that the thermal load of the cooling towers of the Pengze nuclear power plant is about 1.8 times that of the cooling towers of the Amos power plant, and the height of the cooling towers is about 1.7 times, thus the difference in the amount of deposited water caused by the plume of the cooling towers of the two power plants may be related to the thermal load and height of the cooling towers. At the same time, the average annual precipitation in the area of Pengze nuclear power plant site is about 1346.6 mm, and the maximum precipitation caused by cooling tower drift predicted by the SACTI model is much lower than the natural precipitation. The maximum percentage of ground solar loss of Pengze nuclear power plant occurs at 200 m, while the maximum value of ground deposition water occurs at 800 m and 600 m respectively, indicating that the distribution of deposition water lags behind the ground solar loss, which is mainly due to the fact that the cooling tower plume is generally distributed in a relatively high range, and the condensation water continues to drift downwind under the influence of wind.
CFD computational fluid dynamics software is also currently available to carry out environmental impact assessments of cooling tower wet heat plumes.  www.nature.com/scientificreports/ One key difference between CFD and the SACTI model is that CFD is much more computationally-intensive and requires more detailed input data, whereas the SACTI model can be run relatively quickly and with less detailed input information. Additionally, CFD can provide a much more detailed understanding of the fluid dynamics and pollutant dispersion within the cooling tower and its surrounding environment, while the SACTI model is limited in its ability to capture these complexities. However, the SACTI model is a useful tool for quickly assessing the environmental impact of cooling towers in a nuclear power plant setting, and its simplicity makes it accessible to non-experts in fluid dynamics and computational modeling.
In previous research, we use CFD software Star-CCM+ to study the wet deposition of wet heat plumes in cooling towers, with the following analysis process: The results of the CFD and SACTI models were analysed for the axial concentration of wet deposition. In the immediate area, the CFD and SACTI results were relatively close, but beyond 2 km, the SACTI results for wet  www.nature.com/scientificreports/ deposition on the ground decreased more significantly with increasing distance. At 1 km, the CFD results are slightly higher than the SACTI results, mainly because the CFD calculations take into account the effect of the large naturally ventilated cooling tower structures themselves on the dispersion of the plume. The maximum value of CFD ground wet deposition is 2.48E−08 kg/m 2 s, which occurs 300 m downwind of the cooling tower, while the maximum value of SACTI simulated ground wet deposition is 9.65E−09 kg/m 2 s, which occurs 900 m downwind of the cooling tower. The CFD simulation results are significantly more advanced than SACTI, indicating that after taking into account the influence of the cooling tower structures, the large value of ground wet deposition occurs significantly earlier. large values of ground-level wet deposition occur at significantly earlier distances after accounting for the effect of the cooling tower structures.  www.nature.com/scientificreports/ Salt deposition. The following figure shows the average annual salt deposition distribution for the Pengze and Amos nuclear power plants. From Fig. 14, it can be seen that the salt deposition in the Pengze nuclear power plant is mainly distributed in the SW and N directions of the geometric center point of the cooling tower, which is consistent with the surface water deposition shown in Fig. 10. Comparing Figs. 13 and 15 and, the Amos power plant also shows a similar regularity, with the maximum value of salt deposition in the Pengze nuclear power plant occurring at a distance of 800 m from the cooling tower, which is about 166.5 kg/(km 2 ·month), the maximum value of salt deposition at the Amos plant occurs at a distance of 600 m from the cooling tower and is about 92.85 kg/(km 2 ·month). Comparing the salt deposition data of the two plants, it can be found that the regularity of salt deposition and surface water deposition maintain a high degree of consistency. The guidelines for evaluating the effects of salt

Conclusion and prospect
In this research, we use SACTI model to predict the cooling tower plume for the Pengze nuclear power plant in Jiangxi, China and the Amos inland power plant in the United States. The following conclusions have been reached.    www.nature.com/scientificreports/ maximum value of the solar radiation loss caused by the cooling tower plume occurs at a distance of 200 m from the cooling tower in both of the plants. 3. Cooling tower plume caused by the distribution of ground settling water and plume length frequency distribution basically remains constant, ground settling water can be related to the cooling tower heat load and height, while the distribution of ground settling water to lag behind the ground solar radiation loss, which is mainly due to the cooling tower plume is generally distributed at a relatively high height, condensation water continues to drift downwind under the influence of wind. The results showed that the maximum value of salt deposition in the Pengze plant was about 166.5 kg/(km 2 -month) at a distance of 800 m from the cooling tower, and the maximum value of salt deposition in the Amos plant was about 92.85 kg/(km 2 -month) at a distance of 600 m from the cooling tower. The regularity of the amount of salt deposition caused by the cooling tower plume maintains a high degree of consistency with groundwater deposition. Also, according to the predicted results of this paper for the Pengze nuclear power plant, the salt deposition from the large naturally ventilated cooling tower plume of the nuclear power plant will not have a significant impact on the surrounding crops and other plants. In addition, when the annual average wind speed of the plant site is small, the calculation results of the SACTI model will increase significantly, and it will be more obvious in the vicinity of the plant site. This is mainly because the calculation results of the Gaussian model are generally large under the calm wind conditions. Of course, in the SACTI model calculation, the water particle size distribution in the wet heat plume will also significantly affect the wet deposition distribution near the plant site. In general, below 300 microns, the water particles will produce an obvious evaporation process, causing the wet deposition on the ground to become less, while when the particle size is larger than 500 microns, the water particles will rapidly fall to the ground. 4. The SACTI model allows the quantitative calculation and simulation of the effects on the environment of cloud shadows, water and saline discharges during normal operation of large natural ventilation cooling towers in nuclear power plants, based on meteorology data over time. Although the SACTI model does not incorporate topographic correction and computational components, and the results of the simulations can be inaccurate, the results of the model calculations can be used as a basis for assessing the environmental impact of nuclear power plants in the absence of real measurements. 5. In order to provide more accurate information for environmental impact prediction and assessment, which is also the main direction of future research in this research, SACTI model calculation results are stored directly in ASCII code text data, which has no spatial relationship and cannot be used for secondary data analysis by GIS tools, such as spatial overlay with population or land use data. 6. The SACTI model is essentially a Gaussian plume model, which is a widely-used approach for estimating cooling tower plume dispersion. However, the SACTI model still has a number of limitations related to its Gaussian assumptions. For example, it assumes that air pollutant dispersion follows a symmetrical, bellshaped curve from its source, which may not accurately represent real-world conditions. Moreover, the model assumes that air pollutant concentrations decrease exponentially from the source, which may not hold true for sources with non-uniform emissions or where local topography plays a substantial role in dispersion.
Additionally, the SACTI model does not account for some of the more complex atmospheric processes that can impact air pollutant dispersion, such as turbulent mixing or local wind patterns. These limitations should be carefully considered when selecting and applying the SACTI model for environmental impact assessments. Thus, in future, we will use CFD model to carry out research on Cooling Tower plume dispersion.

Data availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.